Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	120,032
2	South Dakota	110,087
3	Wisconsin	87,348
4	Iowa	87,256
5	Nebraska	83,850
6	Utah	82,619
7	Tennessee	80,434
8	Rhode Island	77,467
9	Idaho	76,259
10	Montana	74,645
11	Wyoming	74,642
12	Illinois	74,154
13	Indiana	73,793
14	Kansas	73,171
15	Minnesota	72,543
16	Arkansas	71,203
17	Nevada	70,662
18	Oklahoma	70,543
19	Alabama	70,490
20	Mississippi	69,347
21	Arizona	67,936
22	Missouri	65,858
23	New Mexico	65,798
24	Louisiana	64,473
25	Alaska	62,261
26	Florida	59,222
27	Texas	58,336
28	Kentucky	57,931
29	South Carolina	57,524
30	Georgia	57,506
31	Ohio	57,363
32	Colorado	56,657
33	Delaware	56,540
34	California	54,849
35	New Jersey	52,145
36	Michigan	51,625
37	Massachusetts	51,176
38	North Carolina	49,336
39	Connecticut	48,451
40	Pennsylvania	47,984
41	New York	47,722
42	West Virginia	45,035
43	Maryland	44,239
44	District of Columbia	39,960
45	Virginia	39,080
46	Puerto Rico	33,454
47	Washington	31,653
48	New Hampshire	30,012
49	Oregon	26,017
50	Maine	16,277
51	Hawaii	14,877
52	Vermont	11,264

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	California	1,006
2	Tennessee	826
3	South Carolina	721
4	Delaware	699
5	West Virginia	645
6	North Carolina	617
7	Oklahoma	554
8	Indiana	550
9	New York	550
10	Nevada	540
11	Massachusetts	522
12	Arizona	503
13	Ohio	481
14	Pennsylvania	473
15	New Hampshire	467
16	Alabama	464
17	New Mexico	445
18	Utah	445
19	New Jersey	427
20	Mississippi	418
21	Arkansas	412
22	Georgia	402
23	Kentucky	401
24	Florida	379
25	Virginia	377
26	District of Columbia	361
27	Maryland	356
28	Texas	350
29	Illinois	334
30	Colorado	321
31	South Dakota	318
32	Wyoming	308
33	Alaska	305
34	Wisconsin	279
35	Minnesota	275
36	Nebraska	272
37	Missouri	253
38	Puerto Rico	252
39	Michigan	251
40	Louisiana	231
41	Oregon	230
42	North Dakota	226
43	Montana	222
44	Iowa	173
45	Idaho	167
46	Washington	164
47	Maine	162
48	Vermont	132
49	Hawaii	84
50	Kansas	24
51	Connecticut	0
52	Rhode Island	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,097
2	New York	1,901
3	Massachusetts	1,756
4	North Dakota	1,670
5	South Dakota	1,634
6	Connecticut	1,624
7	Rhode Island	1,608
8	Louisiana	1,568
9	Mississippi	1,547
10	Illinois	1,368
11	Michigan	1,269
12	Iowa	1,186
13	Pennsylvania	1,174
14	Indiana	1,164
15	Arizona	1,157
16	Arkansas	1,153
17	New Mexico	1,119
18	District of Columbia	1,088
19	South Carolina	1,001
20	Florida	987
21	Georgia	975
22	Nevada	962
23	Alabama	956
24	Maryland	944
25	Tennessee	944
26	Texas	934
27	Minnesota	923
28	Delaware	921
29	Missouri	921
30	Wisconsin	865
31	Montana	864
32	Kansas	860
33	Nebraska	818
34	Colorado	809
35	Idaho	759
36	Ohio	727
37	West Virginia	699
38	Wyoming	644
39	Kentucky	636
40	North Carolina	628
41	California	614
42	Oklahoma	598
43	Virginia	568
44	New Hampshire	520
45	Puerto Rico	452
46	Washington	431
47	Utah	378
48	Oregon	340
49	Alaska	262
50	Maine	240
51	Vermont	203
52	Hawaii	199

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	New Mexico	12
2	Illinois	9
3	Michigan	9
4	Arkansas	8
5	Massachusetts	7
6	Pennsylvania	7
7	South Carolina	7
8	New York	6
9	South Dakota	6
10	Arizona	5
11	District of Columbia	5
12	Indiana	5
13	Minnesota	5
14	Mississippi	5
15	North Carolina	5
16	Kentucky	4
17	Maryland	4
18	Nevada	4
19	New Hampshire	4
20	Tennessee	4
21	West Virginia	4
22	Delaware	3
23	Florida	3
24	Georgia	3
25	New Jersey	3
26	Oklahoma	3
27	Vermont	3
28	California	2
29	Colorado	2
30	North Dakota	2
31	Puerto Rico	2
32	Texas	2
33	Virginia	2
34	Alabama	1
35	Louisiana	1
36	Maine	1
37	Missouri	1
38	Montana	1
39	Ohio	1
40	Oregon	1
41	Wisconsin	1
42	Alaska	0
43	Connecticut	0
44	Hawaii	0
45	Idaho	0
46	Iowa	0
47	Kansas	0
48	Rhode Island	0
49	Utah	0
50	Washington	0
51	Wyoming	0
52	Nebraska	-1

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	269,262	1	99
Norton	Kansas	218,429	2	99
Lincoln	Arkansas	214,220	3	99
Dewey	South Dakota	213,001	4	99
Bon Homme	South Dakota	212,578	5	99
Davidson	Tennessee	96,124	319	89
Richland	South Carolina	61,533	1550	50
York	South Carolina	52,007	2044	34
Orange	California	48,344	2216	29
Pierce	Washington	29,474	2820	10

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,587	1	99
Jerauld	South Dakota	7,452	2	99
Dickey	North Dakota	6,568	3	99
Gregory	South Dakota	6,213	4	99
Foster	North Dakota	5,919	5	99
Richland	South Carolina	794	1777	43
Davidson	Tennessee	779	1802	42
York	South Carolina	634	2069	34
Orange	California	581	2168	30
Pierce	Washington	348	2603	17

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.